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AutoAssign: Differentiable Label Assignment for Dense Object Detection

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arxiv 2007.03496 v3 pith:NX2JGVNZ submitted 2020-07-07 cs.CV

Benjin Zhu , Jianfeng Wang , Zhengkai Jiang , Fuhang Zong , Songtao Liu , Zeming Li , Jian Sun This is my paper
classification cs.CV
keywords weightingadjustautoassignobjectachievesadaptassignmentbest
verification ladder T0 review T1 audit T2 compute T3 formal
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Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledge and achieves appearance-aware through a fully differentiable weighting mechanism. During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions. To adapt to object appearances, Confidence Weighting is proposed to adjust the specific assign strategy of each instance. The two weighting modules are then combined to generate positive and negative weights to adjust each location's confidence. Extensive experiments on the MS COCO show that our method steadily surpasses other best sampling strategies by large margins with various backbones. Moreover, our best model achieves 52.1% AP, outperforming all existing one-stage detectors. Besides, experiments on other datasets, e.g., PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign.

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Cited by 4 Pith papers

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  1. DenoDet V2: Phase-Amplitude Cross Denoising for SAR Object Detection

    cs.CV 2025-08 conditional novelty 6.0 of 10

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  2. Physics-Informed Super-Resolution of Atmospheric Data

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  3. SAR-NAS: Lightweight SAR Object Detection with Neural Architecture Search

    cs.CV 2025-09 conditional novelty 4.0 of 10

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  4. CEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects

    cs.CV 2025-06 reject novelty 4.0 of 10

    A tiny-object detector with high-to-low level feature fusion and a sigmoid-weighted L1/L2 loss reports +1.3 AP on AI-TOD, but the loss gradient claims are contradicted by the paper's own equations.

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